Density estimation using a new dimension adaptive particle swarm optimization algorithm

Yan, Yanjun; Osadciw, Lisa Ann

doi:10.1007/s11721-009-0032-x

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…Further, APSO variants have shown promising results [7,15,21,72,74,76,80]. Based on these reasons, this work focuses on APSO and proposes new APSO variants.…”

Section: Optimizationmentioning

confidence: 99%

“…Dynamic dimension optimization problems have positions with a nonconstant number of dimensions, and the dimensionality of the optimum is also unknown [74].…”

Section: Types Of Optimization Problemsmentioning

confidence: 99%

“…A promising research direction within PSO is the use of adaptive concepts and patterns [7,15,21,72,74,76,80]. APSO variants adapt their behavior based on information gathered as the optimization proceeds.…”

Section: Adaptive Particle Swarm Optimizationmentioning

confidence: 99%

“…Dimension adaptive PSO addresses dynamic dimension optimization problems by using a probability equation to calculate the number of dimensions for positions [74]. An adaptive vector is used to determine which dimension is added or deleted in case the number of dimensions of a position changes.…”

Section: Adaptive Particle Swarm Optimizationmentioning

confidence: 99%

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Step-optimized Particle Swarm Optimization

Schoene

Ludwig

Spiteri

2012

2012 IEEE Congress on Evolutionary Computation

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Particle swarm optimization (PSO) is widely used in industrial and academic research to solve optimization problems. Recent developments of PSO show a direction towards adaptive PSO (APSO). APSO changes its behaviour during the optimization process based on information gathered at each iteration. It has been shown that APSO is able to solve a wide range of difficult optimization problems efficiently and effectively. In classical PSO, all parameters are fixed for the entire swarm. In particular, all particles share the same settings of their velocity weights. We propose four APSO variants in which every particle has its own velocity weights. We use PSO to optimize the settings of the velocity weights of every particle at every iteration, thereby creating a step-optimized PSO (SOPSO). We implement four known PSO variants (global best PSO,

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“…Further, APSO variants have shown promising results [7,15,21,72,74,76,80]. Based on these reasons, this work focuses on APSO and proposes new APSO variants.…”

Section: Optimizationmentioning

confidence: 99%

“…Dynamic dimension optimization problems have positions with a nonconstant number of dimensions, and the dimensionality of the optimum is also unknown [74].…”

Section: Types Of Optimization Problemsmentioning

confidence: 99%

Section: Adaptive Particle Swarm Optimizationmentioning

confidence: 99%

Section: Adaptive Particle Swarm Optimizationmentioning

confidence: 99%

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Step-optimized Particle Swarm Optimization

Schoene

Ludwig

Spiteri

2012

2012 IEEE Congress on Evolutionary Computation

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“…The particle swarm optimization (PSO) [4,10,11] exhibits certain similarities with the other evolutionary algorithms (EAs) [2]. The common point of all is that EAs are in population based nature and they can avoid being trapped in a local optimum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Clustering Using Stochastic Approximation Driven Multi-Dimensional Particle Swarm Optimization

Kıranyaz

İnce

Gabbouj

2010

Applications of Evolutionary Computation

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Abstract.With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to the gbest (not to the entire swarm) for a low-cost solution. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.

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